Poor Decision-Making Caused By Overconfidence and Recallability
Poor Decision-Making Traps Include Overconfidence and Recallability

When it comes to making decisions to develop and implement complex systems, where its tentacles touch everything and everyone from a vendor, product, or salesperson, to funds, time, and importance of the system, the sheer number of decisions that one must make can prove challenging. Yet things such as who’s involved in the decision-making process, what information is available, and more importantly who has the final authority in a decision, adds another layer of complexity to the process.

And on top of this, there are also those hidden traps identified by Hammond, Keeney, and Raiffa,[1] that can lead someone down the wrong path if they are not aware of them and their destructiveness to the decision-making process. In this paper we explore the overconfidence and recallability traps, and how they could negatively affect our decisions.


The overconfidence trap is the overestimation of the accuracy of our work, knowledge, forecasts and such. It is “inherent biases built into our brains and bodies.”[2] Moreover, there are two phenomena for overconfidence. The first is the tendency for an individual to believe in their own capacity. The other is the overestimation of preciseness of knowledge.[3]

In all, we tend to believe and feel that what we have done or estimated is correct. If we look at a forecast, we can find an example of overconfidence. The forecast can be anything from say the anticipated cost for the life of the project, to the expected market capture within a year of acquisition, to the likelihood that the company’s bid is high enough to secure the request for quote (RFQ) while at the same time is not low enough to make it unprofitable for them. And if we do forecasts often enough, we may tend to become lazy and not thoroughly analyze the data. We end up making important decisions based on our confidence of having done these types of analysis plenty of times.

Thus, we fall into the overconfidence trap.


The next trap, recallability, is to recall what we have seen, heard, read, and learned in the past and use it for the current decision-making process (DMP). We remember things about items such as products, events, data points that influenced or impacted us in a memorable way. Something was seeded in us that was a result of our own thought process such that we are able to recall vividly bits and pieces of the previously listed items. This overshadows everything else that we might or should know about the decision at hand. Thus, we are quick to make a decision based on the data and information we have stored in our brain.

Omitted is the critical thinking, System 2 Thinking,[4] necessary to make better decisions. For instance, you had a horrific experience on a flight that caused you to think you were moments away from exiting this world. Since then, your emotions got the best of you and when you must travel to another place, you always think first of driving to reach your destination even though flying is safer than driving.[5] If you really thought about the decision, instead of letting what you recall take control of you, you would be more afraid of driving than flying.

Overcoming These Traps

How do we overcome these two traps? First is to recognize that they exist and there is a good chance that we exhibit them. One must admit that they are capable of falling into these traps. We are humans and are fallible. Now, in more specific terms for the overconfidence trap, we must challenge our estimates. We must include the extremes on both ends of the estimates. That is, what are the most and least points and use them accordingly to help us with our forecast.

Monte Carlo Simulation

The use of Monte Carlo simulation is one tool that could help, as well as Bayesian Analysis. Simulation and probability modeling could help us get a better estimate too. Another thing we can do to overcome the first trap is ask other piers, subordinates, or managers to review our estimates with a critical eye. Defy them to find holes in your estimates and the way you used and arrived at your calculations. This latter part may prove to be a bit more humbling as you must accept the criticism of others. Nevertheless, the point of this is to avoid the overconfidence trap by employing the two techniques described. There are more ways to overcome this trap and literature written on this subject that is beyond the scope of this paper.

The recallability trap is more of an internal struggle that you must overcome.

Monitoring Your Assumptions

In your analysis, you must pay close attention to your assumptions and ensure they are in line with the specific problem. Recall what you have read, heard, and learned in the past and make sure that the data or information used by you matches the requirements, the problem statement. That is, you do not want to use data for say the reliability [(Rs(t)] of a laptop when the requirements call for a mainframe computing system. And as before, use modeling and statistics to help with the DMP. To do this, you must have actual data. Try to get it when possible. Guide yourself with data and not impressions.

System 1 Thinking

Lastly, do not fall into System 1 Thinking, fast thinking.[6] This is just taking what you have from your previous experience and data and accepting it. There is no critical thinking. You do not challenge the data and instead, you end up using intuition or heuristics to make the decision.

In short, recognizing that there are traps in the DMP, specifically the overconfidence and recallability trap, you improve your chances of making a good decision. Furthermore, if you apply some of the techniques mentioned in this article to avoid the traps, summarized below, you enhance your DMP and thus will typically get a better outcome.


Techniques for overcoming the Overconfidence Trap:

  • Use extreme points and the appropriate statistical analyses for further enhancement
  • Ask others to review your work in detail

Techniques for overcoming the Recallability Trap:

  • Pay close attention to your assumptions
  • Match the data with the requirements and use modeling and statistics to help with the DMP
  • Avoid System 1 Thinking

[1] Hammond, John S., Ralph L. Kenney, and Howard Raiffa. “The Hidden Traps in Decision Making,” HBR’s 10 Must Reads on Making Smart Decisions. Brighton: Harvard Business Press, 2013, 1–19.
[2] Gordon, W. (2011). Behavioral economics and qualitative research – a marriage made in heaven? International Journal of Market Research, 53 (2), 171-185
[3] Paluch, Dov. “Overconfidence bias in decision-making at different levels of management.” PhD diss., University of Pretoria, 2011, p. 23.
[4] Ibarra, Gerard. Good Decisions, Better Outcomes: A Simple, Five-Step Process to Help You Make Important and Difficult Decisions with Confidence and Clarity. Bublish, 2021, 28-29
[5] Locsin, Aurelio. “Is Air Travel Safer Than Car Travel?” Travel Tips – USA Today. Accessed July 5, 2021. https://traveltips.usatoday.com/air-travel-safer-car-travel-1581.html.
[6] Ibid.